In optical communications systems, an optical signal may be modulated with digital data in order to transmit the data over an optical transmission path. Different parameters of the optical signal may be varied to represent digital data (e.g., the binary digits “0” and “1”). One problem associated with optical communication systems is maintaining the integrity of the data being communicated, particularly when optical signals are transmitted over long distances in long-haul communication systems. Accumulated noise contributed by many different sources in a transmission path may cause degradation of the signals and may cause difficulty in differentiating between the binary digits (i.e., the ones and zeros) in a data stream.
Forward Error Correction (FEC) is a technique used to help compensate for this degradation. FEC is essentially the incorporation of a suitable code into a data stream at the transmitter, for the detection and correction of data errors by the system's receiver. The transmitter receives a data stream and encodes the data stream using an FEC encoder that introduces some redundancy in the binary information sequence of the data stream. The receiver receives the encoded data and runs it through an FEC decoder to detect and correct errors.
Two types of decoding have been used to recover the information bits in the receiver, hard and soft decision decoding. According to hard decision decoding, received samples are compared at the output of the demodulator to an optimal threshold and hard decisions are taken and fed to the decoder where the errors are corrected. For example, a bit is “1” if the signal level exceeds a predetermined level and a bit is “0” if the signal level falls below the predetermined level. According to soft decision decoding, the received samples may be quantized in a multiple bit word and then fed to the decoder. The multiple bits provide “soft” information representing a confidence level in the received data, which may be used to perform more reliable decoding than in the case of hard decision decoding.
Optimization of decision thresholds is desirable in error correction systems. In general, a decision threshold may be considered optimal when the lowest decoded bit error rate (BER) is obtained. In communication systems such as fiber-optic communication systems, however, it may be difficult, if not impossible, to evaluate the BER of customer data and it is also difficult to estimate the decoded BER. In hard detection and decoding systems, the optimal hard-decision threshold may be determined by minimizing the detection error probability. For example, the number of corrected errors reported by the decoder may used in hard-decision systems to estimate the input bit error rate (BER) to the decoder, which should be minimized in threshold adjustment.
However, this method cannot be used to effectively optimize soft-decision thresholds. One reason is that there exists no definition of detection error in soft detection and thus no way to evaluate the detection error probability. In the hard-decision case, adjusting the signal threshold does not affect the FEC capability of the decoder but only changes the input BER, so a minimized input BER corresponds to the best FEC performance and thus the optimal threshold. In multi-bit soft decisions, however, adjusting thresholds affects not only the input BER to the decoder but also the FEC capability of the decoder. From the input BER point of view, the thresholds should be adjusted where least corrected bit errors are reported, while from the FEC capability point of view, maximum number of bit errors should be corrected. Thus, there is a contradiction in minimizing or maximizing corrected bit errors as the criterion for threshold adjustment in a soft-decision system.
Accordingly, there is a need for a system and method of adjusting decision thresholds in a soft decision detection system, which is capable of effectively optimizing the thresholds.